{smcl}
{com}{sf}{ul off}{txt}{.-}
      name:  {res}<unnamed>
       {txt}log:  {res}C:\Users\ondre\OneDrive\Plocha\RnP\Revisions\Dataverse\svr_jeps_replication_log2.smcl
  {txt}log type:  {res}smcl
 {txt}opened on:  {res}20 Jan 2023, 13:19:47

{com}. **************************************************************

. 
. *** Replication code for "The 'Commitment trap' Revisited: ***

. 
. *** Experimental Evidence on Ambiguous Nuclear Threats" by ***

. 
. *** Michal Smetana, Marek Vranka, and Ondrej Rosendorf     ***

. 
. **************************************************************

. 
. 
. 
. *** The code was written in Stata 17.0 BE-Basic Edition ***

. 
. 
. 
. *** Please reach out to ondrej.rosendorf@fsv.cuni.cz if you have any questions concerning this replication file ***

. 
. 
. 
. *** IMPORTANT: This file is accompanied by the svr_jeps_replication_data2 dataset ***

. 
. 
. 
. *** Before proceeding with the replication, please make sure that the "coefplot" and "estout" package is installed ***

. 
. 
. 
. *** To install the coefplot package, use the following command ***

. 
. 
. 
. ssc install coefplot, replace
{txt}checking {hilite:coefplot} consistency and verifying not already installed...
all files already exist and are up to date.

{com}. 
. 
. 
. *** To install the estout package, use the following command ***

. 
. 
. 
. ssc install estout, replace
{txt}checking {hilite:estout} consistency and verifying not already installed...
all files already exist and are up to date.

{com}. 
. 
. 
. *** Setting the output scheme to black and white ***

. 
. 
. 
. set scheme s1mono

. 
. 
. 
. *************************************************

. 
. *** Replication of the results in Appendix 11 ***

. 
. *************************************************

. 
. 
. 
. *** Appendix 11, Figure 1 - Approval (DV), no subset ***

. 
. 
. 
. * Running the ordinal logit model (Model 1)

. 
. ologit approval_ordinal i.scenario_n

{res}{txt}Iteration 0:{space 3}log likelihood = {res:-1188.1148}  
Iteration 1:{space 3}log likelihood = {res:-1186.3974}  
Iteration 2:{space 3}log likelihood = {res:-1186.3969}  
Iteration 3:{space 3}log likelihood = {res:-1186.3969}  
{res}
{txt}{col 1}Ordered logistic regression{col 57}{lalign 13:Number of obs}{col 70} = {res}{ralign 6:730}
{txt}{col 57}{lalign 13:LR chi2({res:1})}{col 70} = {res}{ralign 6:3.44}
{txt}{col 57}{lalign 13:Prob > chi2}{col 70} = {res}{ralign 6:0.0638}
{txt}{col 1}{lalign 14:Log likelihood}{col 15} = {res}{ralign 10:-1186.3969}{txt}{col 57}{lalign 13:Pseudo R2}{col 70} = {res}{ralign 6:0.0014}

{txt}{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}approval_or~l{col 15}{c |} Coefficient{col 27}  Std. err.{col 39}      z{col 47}   P>|z|{col 55}     [95% con{col 68}f. interval]
{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 1}1.scenario_n {c |}{col 15}{res}{space 2}-.2465143{col 27}{space 2} .1331097{col 38}{space 1}   -1.85{col 47}{space 3}0.064{col 55}{space 4}-.5074046{col 68}{space 3}  .014376
{txt}{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 8}/cut1 {c |}{col 15}{res}{space 2}-3.749439{col 27}{space 2} .2428513{col 55}{space 4}-4.225419{col 68}{space 3}-3.273459
{txt}{space 8}/cut2 {c |}{col 15}{res}{space 2}-2.923999{col 27}{space 2}  .173901{col 55}{space 4}-3.264839{col 68}{space 3} -2.58316
{txt}{space 8}/cut3 {c |}{col 15}{res}{space 2}-1.934307{col 27}{space 2} .1275435{col 55}{space 4}-2.184287{col 68}{space 3}-1.684326
{txt}{space 8}/cut4 {c |}{col 15}{res}{space 2}-1.026307{col 27}{space 2} .1069476{col 55}{space 4}-1.235921{col 68}{space 3} -.816694
{txt}{space 8}/cut5 {c |}{col 15}{res}{space 2} .1655718{col 27}{space 2} .0994593{col 55}{space 4}-.0293648{col 68}{space 3} .3605084
{txt}{space 8}/cut6 {c |}{col 15}{res}{space 2} 2.262941{col 27}{space 2} .1458925{col 55}{space 4} 1.976997{col 68}{space 3} 2.548885
{txt}{hline 14}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. 
. 
. 
. * Storing the estimates (Model 1)

. 
. estimates store M1

. 
. 
. 
. * Running the ordinal logit model with controls (Model 2)

. 
. ologit approval_ordinal i.scenario_n i.male c.age c.income i.education_bin i.party

{res}{txt}Iteration 0:{space 3}log likelihood = {res:-1175.9494}  
Iteration 1:{space 3}log likelihood = {res:-1155.1861}  
Iteration 2:{space 3}log likelihood = {res:-1155.1022}  
Iteration 3:{space 3}log likelihood = {res:-1155.1022}  
{res}
{txt}{col 1}Ordered logistic regression{col 57}{lalign 13:Number of obs}{col 70} = {res}{ralign 6:723}
{txt}{col 57}{lalign 13:LR chi2({res:7})}{col 70} = {res}{ralign 6:41.69}
{txt}{col 57}{lalign 13:Prob > chi2}{col 70} = {res}{ralign 6:0.0000}
{txt}{col 1}{lalign 14:Log likelihood}{col 15} = {res}{ralign 10:-1155.1022}{txt}{col 57}{lalign 13:Pseudo R2}{col 70} = {res}{ralign 6:0.0177}

{txt}{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}approval_or~l{col 15}{c |} Coefficient{col 27}  Std. err.{col 39}      z{col 47}   P>|z|{col 55}     [95% con{col 68}f. interval]
{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 1}1.scenario_n {c |}{col 15}{res}{space 2}-.3395217{col 27}{space 2} .1352084{col 38}{space 1}   -2.51{col 47}{space 3}0.012{col 55}{space 4}-.6045253{col 68}{space 3}-.0745182
{txt}{space 7}1.male {c |}{col 15}{res}{space 2} .4457499{col 27}{space 2} .1367709{col 38}{space 1}    3.26{col 47}{space 3}0.001{col 55}{space 4} .1776839{col 68}{space 3}  .713816
{txt}{space 10}age {c |}{col 15}{res}{space 2} .0146653{col 27}{space 2} .0050495{col 38}{space 1}    2.90{col 47}{space 3}0.004{col 55}{space 4} .0047685{col 68}{space 3} .0245621
{txt}{space 7}income {c |}{col 15}{res}{space 2} .0032668{col 27}{space 2} .0192565{col 38}{space 1}    0.17{col 47}{space 3}0.865{col 55}{space 4}-.0344753{col 68}{space 3} .0410089
{txt}1.education~n {c |}{col 15}{res}{space 2} .2566894{col 27}{space 2} .1439878{col 38}{space 1}    1.78{col 47}{space 3}0.075{col 55}{space 4}-.0255214{col 68}{space 3} .5389002
{txt}{space 13} {c |}
{space 8}party {c |}
{space 11}1  {c |}{col 15}{res}{space 2} .6014739{col 27}{space 2} .1702325{col 38}{space 1}    3.53{col 47}{space 3}0.000{col 55}{space 4} .2678244{col 68}{space 3} .9351235
{txt}{space 11}2  {c |}{col 15}{res}{space 2} .2232746{col 27}{space 2} .1699434{col 38}{space 1}    1.31{col 47}{space 3}0.189{col 55}{space 4}-.1098083{col 68}{space 3} .5563575
{txt}{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 8}/cut1 {c |}{col 15}{res}{space 2}-2.711039{col 27}{space 2} .3537217{col 55}{space 4}-3.404321{col 68}{space 3}-2.017757
{txt}{space 8}/cut2 {c |}{col 15}{res}{space 2}-1.851901{col 27}{space 2} .3073705{col 55}{space 4}-2.454336{col 68}{space 3}-1.249466
{txt}{space 8}/cut3 {c |}{col 15}{res}{space 2}-.8350471{col 27}{space 2} .2847096{col 55}{space 4}-1.393068{col 68}{space 3}-.2770266
{txt}{space 8}/cut4 {c |}{col 15}{res}{space 2} .0903272{col 27}{space 2} .2796425{col 55}{space 4} -.457762{col 68}{space 3} .6384165
{txt}{space 8}/cut5 {c |}{col 15}{res}{space 2}  1.33964{col 27}{space 2} .2854543{col 55}{space 4} .7801596{col 68}{space 3}  1.89912
{txt}{space 8}/cut6 {c |}{col 15}{res}{space 2} 3.499337{col 27}{space 2} .3145916{col 55}{space 4} 2.882748{col 68}{space 3} 4.115925
{txt}{hline 14}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. 
. 
. 
. * Storing the estimates (Model 2)

. 
. estimates store M2

. 
. 
. 
. * Generating the coefficient plot (Appendix 11, Figure 1)

. 
. coefplot M1, bylabel(Model 1) || M2, bylabel (Model 2) ||, xline(0) coeflabels(1.scenario_n = "{c -(}bf:Treatment (NFU – Control){c )-}" 1.male = "Gender (male)" age = "Age" income = "Income" 1.education_bin = "Education (university degree)" 1.party = "Party (Democrat – Republican)" 2.party = "Party (Independent – Republican)")
{res}
{com}. 
. 
. 
. * Exporting the coefficient plot (Appendix 11, Figure 1)

. 
. graph export A11F01.png
{txt}{p 0 4 2}
file {bf}
A11F01.png{rm}
saved as
PNG
format
{p_end}

{com}. 
. 
. 
. *** Appendix 11, Table 1 - ordinal logistic regression (Model 1 and 2) ***

. 
. 
. 
. * Generating a table with results (Appendix 11, Table 1)

. 
. esttab M1 M2 using A11T01.rtf, noeqlines eqlabels(none) eform nogaps se pr2 varlabels(1.scenario_n "Treatment (NFU - control)" 1.male "Gender (male)" age "Age" income "Income" 1.education_bin "Education (university degree)" 1.party "Party (Democrat - Republican)" 2.party "Party (Independent - Republican)" _cons "Constant") drop(0.scenario_n 0.male 0.education_bin 0.party cut1 cut2 cut3 cut4 cut5 cut6) mtitle("Approval" "Approval") title(Table 1: Ordinal logistic regression of crisis handling approval) nonumbers mlabels("Model 1" "Model 2")
{res}{txt}(output written to {browse  `"A11T01.rtf"'})

{com}. 
. 
. 
. ************************************************

. 
. *** Continue with svr_jeps_replication_code3 ***

. 
. ************************************************

. log close
      {txt}name:  {res}<unnamed>
       {txt}log:  {res}C:\Users\ondre\OneDrive\Plocha\RnP\Revisions\Dataverse\svr_jeps_replication_log2.smcl
  {txt}log type:  {res}smcl
 {txt}closed on:  {res}20 Jan 2023, 13:20:10
{txt}{.-}
{smcl}
{txt}{sf}{ul off}